#coding=utf-8
#构建感知器

class Perceptron(object):
    def __init__(self,input_num,activator):
        # 权重向量初始化为0
        self.activator = activator
        self.weights = map(lambda _: 0.0, range(input_num))
        # 偏置项初始化为0
        self.bias = 0.0
    def __str__(self):
        return 'weights\t:%s\nbias\t:%f' % (self.weights, self.bias)
    def predict(self, input_vec):
        value = reduce(lambda a, b: a + b,
                       map(lambda (x, w): x * w,
                           zip(input_vec, self.weights))
                       , 0.0) + self.bias
        return self.activator(
            value)

    def _one_iteration(self, input_vecs, labels, rate):
        #样本
        samples = zip(input_vecs, labels)
        for (input_vec, label) in samples:
            output = self.predict(input_vec)
            self._update_weights(input_vec, output, label, rate)
    def _update_weights(self, input_vec, output, label, rate):
        delta = label - output
        self.weights = map(
            lambda (x, w): w + rate * delta * x,
            zip(input_vec, self.weights))
        self.bias += rate * delta
    def train(self,input_vecs,labels,iteration,rate):#输入数据,训练轮数,学习率
        for i in range(iteration):
            self._one_iteration(input_vecs,labels,rate)

# 定义激活函数f
def f(x):
    return 1 if x > 0 else 0
# 构建训练数据
def get_training_dataset():
    # 输入向量列表
    input_vecs = [[1, 1], [0, 0], [1, 0], [0, 1]]
    # 期望的输出列表，注意要与输入一一对应
    # [1,1] -> 1, [0,0] -> 0, [1,0] -> 0, [0,1] -> 0
    labels = [1, 0, 0, 0]
    return input_vecs, labels
# 使用and真值表训练感知器
def train_and_perceptron():
# 创建感知器
    p = Perceptron(2,f)
    input_vecs, labels = get_training_dataset()
    p.train(input_vecs,labels,10,0.1)
    return p

if __name__ == '__main__':
    and_perception = train_and_perceptron()
    print 'Perceptron demo'
    print and_perception
    print '1 and 1 = %d' % and_perception.predict([1, 1])
    print '0 and 0 = %d' % and_perception.predict([0, 0])
    print '1 and 0 = %d' % and_perception.predict([1, 0])
    print '0 and 1 = %d' % and_perception.predict([0, 1])